Publication Type : Journal Article
Publisher : Springer Science and Business Media B.V.
Source : Silicon 14, 11337–11354 (2022)
Url : https://link.springer.com/article/10.1007/s12633-022-01862-w
Campus : Coimbatore
School : School of Engineering
Department : Mechanical Engineering
Year : 2022
Abstract : This study investigates the reciprocating wear behaviour of heat-treated ZrO2 reinforced Al7Si0.3Mg functionally graded composite under unlubricated conditions by varying the heat treatment conditions and applied load using Taguchi’s Design of Experiment methodology. The independent parameters chosen were ageing temperature (145, 165, 185 °C), ageing time (8, 10, 12 h), and applied load (20, 40, 60 N), whereas specific wear rate was the response parameter, as determined through Taguchi’s L27 Orthogonal Array. Wear performance was assessed using a pin-on-flat plate linear reciprocating tribometer. Statistical analysis and the percentage contribution of each process variable to the wear rate characteristics and their importance to the tribological behaviour was defined through ANOVA. Optimum wear rate was obtained at a parametric combination of 20 N load, 165 °C ageing temperature, and 10 h ageing time. The confirmation studies revealed an error percentage of 6 ± 2.5% for heat-treated composite when comparing the performance measures derived by optimal parameter values with experimental data. Analysis revealed that ageing temperature was the most influential factor, followed by applied load and ageing time. Atomic force microscopy and worn morphology analysis on the heat-treated composite revealed severe wear at extreme ageing and applied loads, and mild wear at optimum loading conditions. These composites are best suitable for pump parts and automotive piston-cylinder arrangements which involve reciprocating motion.
Cite this Research Publication : Jojith, R., Radhika, N. & Govindaraju, M. Reciprocating Wear Behavioural Analysis of Heat-treated Aluminium ZrO2/Al7Si0.3Mg Functionally Graded Composite Through Taguchi’s Optimization Method. Silicon 14, 11337–11354 (2022). https://doi.org/10.1007/s12633-022-01862-w